Image determination apparatus and image determination method

ABSTRACT

An image determination apparatus, an image reading apparatus, and an image determining method include a device for cutting a color/monochrome region based on a color saturation value obtained by a predetermined derivation method based on image data, and a device for detecting image errors located in large boundaries of brightness variations including color distortions based on correlations between color saturation and color. When errors are included in the image data acquired by, for example, a color image scanner due to primary color errors at random locations (in particular, color bleeding that occurs as a color distortion in monochrome achromatic color areas), it is possible to extract characteristics from the image data and detect locations in images where color distortions and color bleeding occur without using especially complicated and special compositions to detect where these errors occurred.

BACKGROUND OF THE INVENTION AND RELATED ART STATEMENT

[0001] The present invention relates to technology of apparatuses andmethods which detect color bleeding and color distortions occurringadjacent to monochrome areas of pictures read while a transfer speed ofa document is fluctuating, and relates to an image reading apparatusthat reads documents while moving relative to the document and readingmeans, particularly the present invention comprises an image sensor thathas, for example, a primary color sensor line and an image readingapparatus that simultaneously reads documents in three colors to acquireimage data.

[0002] As a single-package image sensor, there are so-called three-linecolor sensors that have three lines of sensors which are equipped with aprimary color filter in the photoelectric transducers of each line. Notonly can this three-line color sensor comprise an image readingapparatus (hereinafter referred to as color scanner) that reads colorand be compact in size compared to using three one-line only imagesensors, but also the manufacturing processing of the manufacturer isadjusted in order that the gaps between the three lines are within thedesign requirements. Consequently, position adjustments for users aresimplified. Another advantage is the fact that it can read colordocuments at high speeds while sequentially lighting a primary colorlight source compared to the so-called light source switching method.

[0003] There are two representative types of color scanners which usethis type of three-line color sensor. One of these is called a fixeddocument type color scanner that reads documents secured onto a glasssurface while moving an optical read system, and the other one is calleda sheet pass type color scanner that has a fixed optical read system andreads documents by conveying the document being conveyed by conveyancemeans.

[0004] Sheet pass type color scanners which feed one sheet at a timefrom a stack of documents and then read the documents as they movethrough the scanner lack stability and have fluctuations in the imagesdue to ripples and fluttering in the conveyance process of the documentsor shocks caused by documents separating from the retaining point (nippoint) of the transfer roller used to convey documents. In addition,shapes containing false errors in the original picture also occur asimage data.

[0005] A sheet pass type includes a sheet pass color scanner thatintegrates the conveyance unit into the apparatus as well as a type ofscanner in which a document feeding apparatus that separates and feedsdocuments from a document stack one at a time (hereinafter referred toas ADF: automatic document feeder) is installed in the above-mentionedfixed document type color scanner. In order to respond to recent demandsfrom users who want to read double-sided documents, a composition hasalso emerged that holds the image reading unit within the ADF and readsboth sides of the document on the color scanner and the ADF.

[0006] A fixed document type color scanner that fixes and readsdocuments does not exhibit unstable behavior in documents and is assumedto be able to provide stable document reads. Even with fixed documenttypes, in a color scanner with a high-resolution read, vibrations andoscillations exist in the sub-scanning direction (direction of movement)or shuddering traversing the reading direction of the optical carriagethat integrates a mirror carriage and optical system to form read lines,appear in the image just like when there are ripples in a document.

[0007] When abnormal images, which are false pictures, are contained inoriginal pictures at random locations within an image acquired in thismanner (in particular, color distortions and color bleeding occurring atmonochrome achromatic color areas), it is difficult to detect wherethese images exist, thereby increasing the desire for a definitivesolution. Thereupon, an object of the present invention is to take theseproblems into consideration and provide an apparatus and a method thatcan extract characteristics from already acquired image data and detectthe locations in the images where color distortions and color bleedingoccur without using an especially complicated or special configuration.

[0008] The read position differs on the surface of a document in aso-called three-line simultaneous read system that uses a three-linecolor sensor in which photoelectric transducers within one package arearranged in three primary color lines to read documents. Because ofthis, when the document is not conveyed evenly, the read position on theimage data will become inconsistent with the intervals in thecomposition of the image sensor, resulting in the occurrence of blurringin the image. The position of the read lines which read the threeprimary colors of the document surface can be made to coincide in orderto avoid this. Japanese Patent Publication (Tokkai) No. 07-143281 (priorart-1) is an example from this point of view. In this example, adispersion element such as a prism is placed in the optical path in anoptical reduction system. This method, however, requires the dispersionposition to coincide with the positions of each color line of the imagesensor, thereby requiring very precise adjustments which in turn leadsto considerable manufacturing processes and in particular makes theadjustment process complicated making this an undesirable method.

[0009] Japanese Patent Publication (Tokkai) No. 08-163316 (prior art-2)teaches a device wherein a document is read and the image data analyzedwith black regions being detected from each color data. This isperformed together with the brightness varying to a large degree,namely, the focal point targeting high contrast areas (edge regionsbetween white and black), a determination being made as to whether ornot the locations of each of the three primary colors coincide at thislocation and the color distortion portion being detected and corrected.In this disclosure, however, only the utilization of brightness for themethod to detect black locations was disclosed while a detection methodwith good achromatic color accuracy is not.

[0010] Furthermore, Japanese Patent Publication (Tokkai) No. 08-139949(prior art-3) teaches technology wherein a correlation function is usedas a detection method of color saturation regions. This document alsodisclosed that when peak values and the total value of the correlationfunction are observed, it is possible to distinguish between chromaticcolor regions due to position read errors or fundamental low-frequencychromatic color regions (color areas) and improve the performance ofread error separation from similarly shaped original images because ofequivalence due to auto-correlation, for example, read errorsaccompanying simple shapes such as thin lines. The fact thatauto-correlation determination must be introduced results in acomposition that governs calculations.

[0011] The first object of the present invention is to build a colorsaturation means that identifies color saturation regions based oncalculations which use both original image data values and negativeimage data values in order to obtain color saturation detection withoutusing cross-correlation that requires an execution time. The firstobject of the present invention is also to provide an imagedetermination apparatus equipped with a color bleed determination meansthat detects whether color bleeds are present at locations the colorsaturation means has identified as monochrome regions and an imagereading apparatus and image determination method equipped with thisimage determination apparatus.

[0012] The second object of the present invention is to build a colorsaturation means that identifies color saturation regions from themagnitude of cumulative values after L * a * b * conversion of the threeprimary colors R, G and B using table conversion in order to obtaincolor saturation detection without using cross-correlation that requiresan execution time. The second object of the present invention is also toprovide an image determination apparatus equipped with a color bleeddetermination means that detects whether color bleeds are present atlocations the color saturation means has identified as monochromeregions and an image reading apparatus and image determination methodequipped with this image determination apparatus.

[0013] The third object of the present invention is to divide examinedregions based on the magnitude of deviation values along withintroducing distributed arithmetic for the purpose of standard deviationcalculations when calculating correlations to determine whether highcolor saturation color bleeding is present after obtaining a division ofthe color/monochrome regions. The third object of the present inventionis also to provide an image determination apparatus comprising asimplified error detection means and an image reading apparatus andimage determination method equipped with this image determinationapparatus.

SUMMARY OF THE INVENTION

[0014] In order to achieve the above-mentioned objects, as the firstaspect the present invention provides an image determination apparatuscharacterized by comprising an image data receiving means that receivesimage data comprising picture elements (pixels) formed from RGB (threeprimary colors of light), a first comparing means that compares a firstcolor saturation value determined from color components of each pixel ofthe above-mentioned received image data to a first threshold value, areversed image calculation means that calculates the color components ofreversed images, or negative images, acquired by subtracting theabove-mentioned color components from the maximum value of the colorcomponents of each of the above-mentioned pixels that can be obtained, asecond comparing means that compares a second color saturation valuedetermined from color components of this reversed image to a thresholdvalue, and a color region determination means that determines whetherthere is color or monochrome based on the result of the comparisonbetween the first comparing means and the second comparing means.Consequently, the present invention achieves region separation in orderto simplify uneven divisions of color saturation values and obtain asimple processing circuit and achieve a region determination usingprograms as well as to quickly and easily detect high color saturationlocations existing close to monochrome areas (none or low color regions)of color images.

[0015] In addition to the above-mentioned image determination apparatus,the present invention provides a brightness change detection means thatfinds color saturation values based on maximum values and minimum valuesin the color components of the above-mentioned pixels and then examinesany changes in brightness from values of the color components of thepixels for regions the above-mentioned color region determination meanshas determined to be monochrome, an edge detection means that detectsexcessive change points in brightness obtained by comparing the detectedvalue from the brightness change detection means and a specified value,a color saturation recalculation means that recalculates colorsaturation values at excessive change points in brightness detected bythe edge detection means, and an error detection means that compares theresult of the above-mentioned color saturation recalculation means to aspecified value to determine errors in image data by counting the numberof consecutive pixels which exceed a specified value. Consequently, thepresent invention achieves quick and easy detection of high colorsaturation locations existing close to monochrome areas (none or lowcolor regions) of color images.

[0016] The second aspect of the present invention can also be an imagedetermination apparatus characterized by comprising an image datareceiving means that receives image data comprising picture elements(pixels) formed from RGB (three primary colors of light), a colorconversion means that converts RGB color space of image data received bythe image data receiving means to a uniform perceived color space thatcomprises brightness values in proportion to luminous intensity andstandardized perceived color values in proportion to human coloring, anaccumulation means that accumulates perceived color values of image dataconverted by the color conversion means in each small region comprisedby specified pixels, and a color region determination means thatdetermines whether the small regions are color or monochrome based onthe accumulated value. Consequently, the present invention achievesregion separation in order to simplify uneven divisions of colorsaturation values and obtain a simple processing circuit and achieve aregion determination using programs as well as to quickly and easilydetect high color saturation locations existing close to none or lowcolor regions of color images.

[0017] In addition to the above-mentioned image determination apparatus,the image determination apparatus can be characterized by an errordetermination means wherein the above-mentioned color conversion meansperforms conversions to CIE 1976 standard L*a*b* uniform perceived colorspace as well as determines whether the small regions are color ormonochrome based on the accumulated value of the a* value and/or the b*value of the image data converted to the uniform perceived color spacewithin the small regions and determines errors in image data based onthe accumulation of perceived color values in regions even smaller thanthe above-mentioned regions including target pixels within smallmonochrome regions and in addition, the above-mentioned errordetermination means determines if an error exists when the cumulativevalue of the a* value or the cumulative value of the b* value of thetarget pixels are outside the range of a specified value. Consequently,the present invention achieves quick and easy detection of high colorsaturation locations existing close to monochrome (none or low colorregions) of color images.

[0018] The third aspect of the present invention is an imagedetermination apparatus that can be characterized by an imagedetermination apparatus equipped with a color discrimination means thatreceives image data comprising picture elements (pixels) formed from RGB(three primary colors of light) and determines whether the target pixelsof the discrimination in the regions of the above-mentioned image datawhich are divided up into small regions are color or monochrome.Further, this color discrimination means comprises an errordetermination means that determines whether there is color or monochromein the above-mentioned small regions determined to be monochrome basedon values of three types of standard deviations which use two reciprocalprimary colors of three color components using the pixels of the smallregions and also determines if errors exist in image data based on threetypes of correlated values which use two reciprocal primary colors ofthree color components of the discrimination target pixels. Evenfurther, the color distortion detection means calculates the correlatedvalues as candidates for error determination when the values of theabove-mentioned standard deviations exceed a specified value.Consequently, the present invention achieves quick and easy detection ofhigh color saturation locations existing close to monochrome areas (noneor low color regions) of color images.

[0019] The fourth aspect of the present invention can provide an imagereading apparatus that can detect and forcibly correct for errors evenif there is an occurrence of a high color saturation border (colordistortion) in a monochrome area by comprising an image readingapparatus equipped with these image determination apparatuses.

[0020] The fifth aspect of the present invention can be an imagedetermination method characterized by a receiving process that receivesimage data accepting image data that receives image data comprisingpicture pixels formed from RGB which are the three primary colors oflight, a first comparison process that compares a first color saturationvalue determined from color components of each pixel of theabove-mentioned received image data to a first threshold value, areversed image calculation means that calculates the color components ofreversed images acquired by subtracting the above-mentioned colorcomponents from the maximum value of the color components of each of theabove-mentioned pixels that can be obtained, a second comparing meansthat compares a second color saturation value determined from colorcomponents of this reversed image to a threshold value, and a colorregion determination means that determines whether there is color ormonochrome based on the result of the comparison between the firstcomparing means and the second comparing means. Consequently, thepresent invention achieves region separation in order to simplify unevendivisions of color saturation values and obtain a simple processingcircuit and achieve a region determination using programs as well as toquickly and easily detect high color saturation locations existing closeto monochrome areas (none or low color regions) of color images.

[0021] The sixth aspect of the present invention can be an imagedetermination method characterized by comprising an image data receivingprocess that receives image data comprising picture elements (pixels)formed from RGB (three primary colors of light), a color conversionprocess that converts RGB color space of this received image data to auniform perceived color space that comprises brightness values inproportion to luminous intensity and standardized perceived color valuesin proportion to human coloring, an accumulation process thataccumulates perceived color values of image data converted by the colorconversion process in each small region comprised by specified pixels,and a color region determination process that determines whether thesmall regions are color or monochrome based on the accumulation resultof the accumulation process. Consequently, the present inventionachieves region separation in order to simplify uneven divisions ofcolor saturation values and obtain a simple processing circuit andachieve a region determination using programs as well as to quickly andeasily detect high color saturation locations existing close tomonochrome areas (none or low color regions) of color images.

[0022] Furthermore, the seventh aspect of the present invention can bean error determination method characterized by comprising adetermination process that determines whether there is color ormonochrome in the regions determined to be monochrome by theabove-mentioned color region determination means based on values ofthree types of standard deviations which use two reciprocal primarycolors of three color components using the pixels of the small regionsof a specified number of pixels, and an error determination process thatdetermines if errors exist in image data based on three types ofcorrelated values which use two reciprocal primary colors of three colorcomponents of the discrimination target pixels. Consequently, thepresent invention achieves quick and easy detection of high colorsaturation locations existing close to monochrome areas (none or lowcolor regions) of color images.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023]FIG. 1 shows an example when using color distortion detectionwithin the computer 2 or the color scanner 1;

[0024]FIG. 2 shows color distortion detection independently comprised asan image determination apparatus;

[0025]FIG. 3 shows a state of a transfer roller that generates colordistortions;

[0026]FIG. 4 shows RGB primary color values when color distortionsoccur;

[0027]FIG. 5 shows locations where brightness due to color saturationand threshold values for determination are added in a brightness plot;

[0028]FIG. 6 shows a conceptual division of types of image data includedin image data acquired by a color scanner;

[0029]FIG. 7 illustrates a relationship between a degree of perceivedcolor and color using the CIE L*a*b* method;

[0030]FIG. 8 shows an illustration of an edge where color distortionoccurs in a direction of travel of a paper using the CIE L*a*b* methodand a Sobel filter;

[0031]FIG. 9 shows a collection of threshold values used in acolor/monochrome separation using the CIE L*a*b* method;

[0032]FIG. 10 shows an original image in A1, results of correlation inA2, results of color saturation in A3 and results of the CIE L*a*b*method in A4;

[0033]FIG. 11 shows the first half of a flow of color saturation;

[0034]FIG. 12 shows the second half of the flow of color saturation;

[0035]FIG. 13 shows an outline of an overall flow of the CIE L*a*b*method;

[0036]FIG. 14 shows the first half of the flow of the CIE L*a*b* method;

[0037]FIG. 15 shows the second half of the flow of the CIE L*a*b*method;

[0038]FIG. 16 shows a flow of the correlation method;

[0039]FIG. 17 shows a color scanner that includes an ADF that is thefourth aspect;

[0040]FIG. 18 shows a block diagram of a color scanner that includes anADF;

[0041]FIG. 19 shows an outline of a flow of a color scanner thatincludes an ADF;

[0042]FIG. 20 shows a block diagram of an image determination apparatus;

[0043]FIG. 21 is a diagram showing a composition of the CIE L*a*b*method used in a neural network; and

[0044]FIG. 22 shows an outline of a composition of an imagedetermination apparatus that uses color saturation.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0045] Exemples of embodiments of an image reading apparatus and animage reading method related to the present invention will be describedwhile referring to the drawings. In addition, an embodiment of an imagereading apparatus with color distortion detection that can be applied tothe present invention will also be described while referring to thedrawings.

[0046] As an example, an image sensor, such as a CCD or CMOS, that has athree-line three primary color photoelectric transducer within onepackage is used in the image reading apparatus (flat scanner) as shownin FIG. 17.

[0047] In the color scanner of FIG. 17, an ADF (automatic documentfeeder) is installed to make this a sheet pass color scanner that feedsand conveys documents one sheet at a time and then reads them as theypass through the scanner. There is a lack of stability and fluctuationsoccur while a document is being conveyed in this manner due to ripples,oscillations and fluttering or shocks caused by documents separatingfrom the retaining point of the transfer roller used for documentconveyance or collisions with the members comprising the conveyancepath. (Refer to FIG. 3.)

[0048] The fixed document type color scanner in which a carriage 56,built into the color scanner of FIG. 17, moves and reads a fixeddocument is not considered to exhibit unstable behavior in documents andis assumed to be able to provide stable document reads. In a colorscanner with a high-resolution read of, for example, 600 DPI, even afixed document type, vibrations and oscillations in the sub-scanningdirection (direction of scanning) or shuddering in the crosswise readingdirection of the optical carriage that integrates a mirror carriage thatforms read lines, and an optical system appear in the image just likewhen ripples in a document exist.

[0049] This color scanner can scan the surface of a document once andacquire three color components one time. It also reads the color of thelight source while switching through three colors. This scanner is alsocalled a sequential surface color scanner. It has a line sensor withouta one-line filter and sequentially acquires three surfaces whilechanging filters.

[0050] There are many examples of acquiring images with this type ofcolor scanner depending on the means to acquire the images. For example,the positioning of the read movement might be incorrect due to externalimpacts even in a sequential surface color scanner. This is referred toas a color distortion (color distortions are included in image data).

[0051] In this manner, detecting where the position of color distortionexists from image data that includes color distortion is difficult. Thereason why is because the document more than likely contains colorthereby making it difficult to classify whether there is colordistortion or the original color.

[0052] If, however, color distortion occurs in the border of anachromatic color region (monochrome) existing within image data, it willbe very noticeable and probably can be dealt with by resolving theerror. Thereupon, if color is found in the area where the contrast ischanging in an achromatic color region, properties will be used whichcan be obtained by means of distinguishing whether there is an originalcolor image or color due to color distortion.

[0053] A method will be described that detects color distortion bycalculating pixel coloring and negative pixel coloring and thenspecifying a region as a fifth embodiment that corresponds as a methodin the embodiment of a first determination apparatus. An outline of themethod that uses this color value is as follows.

[0054] Color distortions are noticeable when at least one color of abrightness value within RGB differs greatly from two other colors of abrightness value. Since the noticeable area is monochrome, themonochrome area is the target. The color distortion occurs in thedirection of the paper feed and the influence of one error spreads to anumber of pixels.

[0055] In other words, a large amount of color saturation occurs inborder regions where the contrast within the monochrome regions changesgreatly for pixels visible as color distortions when viewed by the humaneye. Further, the determination of whether there is an original colorimage or a large amount of color saturation due to color distortionscompares values decided on in advance and then performs thedetermination for which pixels continuously have a large amount of colorsaturation. Consequently, areas where the magnitude of the colorsaturation of the number of pixels is high will be detected.

[0056] Although there is a process for detecting color distortions,color saturation S and SR defined in the equation will be used.$\begin{matrix}{S = \frac{{\max \left( {R,G,B} \right)} - {\min \left( {R,G,B} \right)}}{\max \left( {R,G,B} \right)}} & \text{(1-1)} \\{S_{R} = \frac{{\max \left( {R,G,B} \right)} - {\min \left( {R,G,B} \right)}}{255 - {\min \left( {R,G,B} \right)}}} & \text{(1-2)}\end{matrix}$

[0057] The following relationship is formed from equations (1-1) and(1-2). $\begin{matrix}{S = {\frac{S_{R}}{1 - S_{R}}\left( {\frac{1}{V} - 1} \right)}} & \text{(1-3)}\end{matrix}$

[0058] Here, the brightness is as follows.

V=max(R,G,B)/255   (1-4)

[0059] The variables S, SR and V above are defined in Image AnalysisHandbook, Mikio Takagi & Yohisa Shimoda, p.475-491, 1991, 1, 17 (theTokyo University Publishing).

[0060]FIG. 5 is a distribution map. In the figure the X-axis isbrightness V determined by equation (1-4) above and the Y-axis is colorsaturation S determined by equation (1-1) above. The values each pixelpossesses are plotted in the figure. When threshold value TSR isstipulated for SR, that TSR will become the curve as shown in FIG. 5.

[0061] Color distortion detection is performed as follows. This processis equivalent to utilizing the remainder after removing the thresholdvalue TS (or less) or the left side of the threshold value TSR (orless).

[0062] (1) Find color saturation S of each pixel.

[0063] (2) Color distortion candidate when S is threshold value TS ormore.

[0064] (3) Find reversed image SR.

[0065] (4) Color distortion candidate when SR is threshold value TSr ormore.

[0066] (5) From the extracted candidates count the number of pixels ofthe candidates with sequential pixels extracted as color areas in thedirection of travel of the paper and when that number is equal to orless than N pixels, detect the result as a color distortion. This isdescribed using FIG. 11 and FIG. 12. When the sequential number islarge, there is no color distortion and there is a color region. Thislarge suitability assumes that the amount of color distortion of theimages which include color distortion will be determined in advance.

[0067] A description of a color scanner that includes a rear ADF will bedescribed referring to FIG. 19 for image acquisition input to S300. Thisis a step that receives image data read and saved by a color scanner.The number of pixels in the horizontal direction j and the number ofpixels in the vertical direction i as well as the number of primarycolors k are provided at the same time. The purpose of this is to copyand detect images read and saved by the color scanner onto an offlinecomputer or similar device. No data in particular is provided in onlineprocesses or processes internal to an apparatus referred to asstand-alone and variables exist which allow internal transfers.

[0068] S301 and S302 are process loops formed by S312 and S318˜S321. Theprocess initializes variables in S303 and S304 and finds colorsaturation S of the original enlarged image in S305˜S307. By comparingthis result to the threshold value TS in S308, a determination is madeon whether it is a candidate for a color distortion. If the thresholdvalue is exceeded, it is a candidate. If it is not a candidate, the loopwill repeat until completing all image data.

[0069] Next, the process moves to reversed image determination. Colorsaturation SR of a reversed image is calculated in S309 using the itemswhich were determined to be candidates above. Then, in S310, candidatesfor color distortions are decided on when the calculated colorsaturation value of the reversed image value exceeds the threshold valueTS. Thereafter, the variable count increments in S311 as a candidatedetermination process. When the color saturation drops and B is removedafter this candidate process is repeated as a loop, a determination ismade as to whether a color distortion was detected from S314 to S317 andthen moves to S313. When the result is larger than specified value N, adetermination will be made that there is a color region and there is nocolor distortion. When all the pixels have completed the processing, theprocess will quit in S322. At this time flag processing is performed inS315. Using this flag after the processing allows correction processingto be performed.

[0070]FIG. 22 shows an outline of the composition of an imagedetermination apparatus that is dependent on color saturation utilizingthis reversed image data. The individual circuit compositions can becomprised of commonly known technology. In addition, althoughdeterminations are made using specified threshold values for areas wheredeterminations are performed for the entire area, each threshold valuesmust be adjusted to an optimum value in response to the target images orthe degree of color distortion contained in the image. Consequently,although this is omitted in FIG. 22, these specified values can normallybe stored in non-volatile memory as parameters and if necessary,determined based on a variety of measurement values within themanufacturing process.

[0071] An example using the CIE L*a*b* method will be described as thesixth embodiment as a method that corresponds to an embodiment of asecond determination apparatus.

[0072] In this method color distortion is noticeable in monochrome areasof images. The color distortion is also noticeable in high contrastareas (edge areas) in images.

[0073]FIG. 6 shows an image read into a color scanner divided updepending on properties. The figure is a conceptual diagram showingimage data distributed in two dimensions divided up into regions of eachproperty. The figure is an enlarged view divided into color areas andmonochrome areas.

[0074] Areas with high contrast (edge areas) exist in any of these.Areas with reading errors exist in the same manner but areas withconspicuous color distortion are in the slanted line area of FIG. 6.

[0075] In this method, generally, this slanted line area is detected instep (1) separation of color and monochrome, step (2) edge detection andstep (3) color distortion determination.

[0076] The detection procedure initially separates the color andmonochrome. In order to distinguish between color and monochrome, CIEL*a*b* values (CIE standard) close to the degree of human perceivedcolor are used. Conversion calculations of CIE L*a*b* color coordinatesystems are as follows.

[0077] First, the color coordinate system is converted to an XYZ colorcoordinate system using formula (2-1). $\begin{matrix}{\begin{pmatrix}X \\Y \\Z\end{pmatrix} = {\begin{pmatrix}0.49000 & 0.31000 & 0.20000 \\0.17697 & 0.81240 & 0.01063 \\0.00000 & 0.01000 & 0.99000\end{pmatrix}\begin{pmatrix}R \\G \\B\end{pmatrix}}} & \text{(2-1)}\end{matrix}$

[0078] Then, it is converted to a CIE L*a*b* color coordinate systemusing formula (2-2). $\begin{matrix}\left\{ {\begin{matrix}{L^{*} = {{166\left( {Y/Y_{n}} \right)^{1/3}} - 16}} \\{a^{*} = {500\left\lbrack {\left( {Y/Y_{n}} \right)^{1/3} - \left( {Y/Y_{n}} \right)^{1/3}} \right\rbrack}} \\{b^{*} = {200\left\lbrack {\left( {Y/Y_{n}} \right)^{1/3} - \left( {Y/Z_{n}} \right)^{1/3}} \right\rbrack}}\end{matrix}\quad} \right. & \text{(2-2)}\end{matrix}$

[0079] Now the following result is obtained. $\left\{ {\begin{matrix}{X_{n} = 95.045} \\{Y_{n} = 100.00} \\{Z_{n} = 108.892}\end{matrix}\quad} \right.$

[0080] It is obvious that the processes for the variables above areimportant, but in reality, these are not real number processes whichadhere to the formulas. They can quickly convert from an RGB color spaceto a CIE L*a*b* color coordinate system in one step by means of a methodthat uses a neural network to perform approximate conversions or byaccumulating conversion relationships as numeric tables. Because ofthis, real-time comparisons to calculations of correlated values becomea simple process. FIG. 21 shows a representative composition when usinga neural network. Each section can use conventional circuits.

[0081]FIG. 7 is a chromaticity diagram of a CIE L*a*b* color coordinatesystem. In the figure the a* axis and b* axis intersect at a right anglewith the point of intersection forming an achromatic color point(monochrome point). An achromatic color (monochrome) region can beextracted by means of determining the range of the a* value and b*value.

[0082] When there is no color distortion, separating the color andmonochrome is simple. When there is color distortion, however,separating the color and monochrome areas in pixel units is difficultbecause the properties of the color and color distortion are similar.Stated in a different way, it is difficult to divide differences of thecolor and color distortion using only color. Thereupon, the followingalgorithm separates the color and monochrome.

[0083] (1) Separates pixels in regions of m X m.

[0084] (2) Creates an accumulation histogram of the a* values and b*values in the vertical direction (sub-scanning direction) in the regionsof (1). The accumulation values are normalized by the number ofaccumulated pixels. This examines the height of the color saturation.

[0085] (3) Extracts the accumulation values in (2) which are in therange of values of Ta_(L)<a*<Ta_(H) and also Tb_(L)<a*<Tb_(H) asmonochrome regions.

[0086] (4) Creates an accumulation histogram of the a* values and b*values in the horizontal direction (primary scanning direction) only forthe areas not determined to be monochrome in (3) in order to extract themonochrome regions. The accumulation values are normalized by the numberof accumulated pixels here as well.

[0087] (5) Extracts the accumulation values in (4) which are in therange of values of Ta_(L)<a*<Ta_(H) and also Tb_(L)<a*<Tb_(H) asmonochrome regions.

[0088] Since this is stated based on the assumption that theabove-mentioned identification process of the achromatic color regionsperforms the processing after accumulating the surface image data,whether the primary scanning direction or the sub-scanning direction isfirst is not important. It is preferable, however, for the primaryscanning direction to take precedence when performing the process closeto real-time in an amount of buffer memory as small as possible.

[0089] Monochrome regions are determined by the above-mentionedprocesses. Next, edges (high contrast border areas) of monochromeregions will be detected.

[0090] As shown in FIG. 8, because color distortion occurs in thedirection of travel of the paper (sub-scanning direction), the edge inthe horizontal direction is detected using a vertical direction Sobelfilter. A Sobel filter is a type of differentiation filter.

[0091] Now determination of color distortion will be described. Indetected edge areas, the pixels in the range of values ofTa_(L)<a*<Ta_(H) and also Tb_(L)<a*<Tb_(H) belong to the achromaticcolor region at each pixel. Closely examining these regions makes itpossible to extract the color distortion.

[0092] In the method described here, the image that is used is an imageacquired by a sheet pass format and color distortions occur in thetravel direction, namely, the conveyance direction, of that paper.Consequently, in order to simplify the observations, detections arelimited to the vertical direction. Changing the algorithm to thehorizontal direction will also allow use in both directions.

[0093]FIG. 9 is a compilation of each threshold value used in thedetermination. These threshold values are merely samples. The detectionsensitivity can be changed by adjusting the threshold values sopreparing several threshold values in a table format will naturally makeit possible to change the choices depending on the target image such asillustrations, posters, nature images or portraits.

[0094] Next, a detailed description will be provided referring to FIGS.13, 14 and 15. FIG. 13 shows an overall outline, and S305 and S357 areI/O to this process. In S351, a conversion process to a CIE L*a*b* colorcoordinate system is performed. After this, in S352, the area of theentire original image divides into specified small regions (for example,50*50). The purpose of this is to create an accumulation histogram ofthe a* values and an accumulation histogram of the b* values. In S354,the color and monochrome separate using the result of the accumulationhistogram. In S355, edge detection within the monochrome region isperformed and then the final determination of color distortion isperformed in S356.

[0095]FIGS. 14 and 15 provide details of the above-mentioneddescription. S360 and S379 are I/O to this process. In S361, the colordistortion position flag is cleared. In S362 and S363, a conversionprocess to a CIE L*a*b* color coordinate system is performed. In S364and S365, the area of the entire original image divides into specifiedsmall regions. S366 creates a histogram for each small area.Accumulation histograms of the a* values and b* values are prepared. Ifmonochrome is determined in S367 and S368, a flag is raised in S369. InS370 and S371, if monochrome is determined for only the area where theflag was raised, the flag will then be confirmed in S372. In S373, edge(corresponding to determination of contrast) detection within themonochrome region is performed. Then, in S374, S375, S376, S377 andS378, the target pixels (periphery of pixels) will be color within themonochrome area resulting in a determination of color distortion.

[0096] Next, the seventh embodiment corresponding to the method in thethird determination apparatus will be described. This embodiment makesit possible to detect color distortion even if correlations arecalculated within three primary colors.

[0097] In a correlation method, the next characteristic is focused on,and color distortion is detected. The target image, however, is limitedto monochrome images. Consequently, the color and monochrome areseparated in advance using the method already described, and the methoddescribed below will become applicable in regions where monochrome isrecognized.

[0098] If, however, indications, such as a monochrome document, aregiven beforehand, the method can be applied even if the above-mentionedseparation is not performed.

[0099] This method is realized on the assumption of the followingproperties.

[0100] (1) Color distortion is noticeable in high contrast areas in animage.

[0101] (2) Color distortion has different RGB brightness values.

[0102] (3) When color distortion occurs in high contrast areas thebrightness values of the distorted colors within RGB are replaced withbrightness values of the adjacent pixels. Because of this, there is noconsistency with brightness changes of RGB.

[0103] Next, the detection process of this method will be described.

[0104] The purpose is to detect colors which are noticeably distortingin a correlation coefficient between two colors. Because the colordistortion occurs in the direction of travel of the paper (sub-scanningdirection), the correlation coefficient is found from formula (3-1)along the direction of travel of the paper, where, {x, y}={R, G, B}, wis a size of region which has the correlation. $\begin{matrix}{{Rxy} = \frac{\sum\limits_{i = N}^{N + {({w - 1})}}{\left( {x_{i} - \overset{\_}{x}} \right)\left( {y_{i} - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{i = N}^{N + {({w - 1})}}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}\sqrt{\sum\limits_{i = N}^{N + {({w - 1})}}\left( {y_{i} - \overset{\_}{y}} \right)^{2}}}} & \left( {3 - 1} \right)\end{matrix}$

[0105] In low contrast areas the value of the denominator of the formula(3-1) decreases and errors increase making it unsuitable to use thevalues of the correlation coefficient. Therefore, the correlationcoefficient is only calculated for areas larger than the threshold valueTD (the value of the denominator). When the area is smaller than thethreshold value TC (correlation coefficient), it will be detected ascolor distortion.

[0106] Color distortion is detected as follows.

[0107] (1) Calculates the value of the denominator of the formula (3-1).

[0108] (2) Calculates the correlation coefficient when the denominatoris larger than the threshold value TD.

[0109] (3) Determines that color distortion exists in those pixels whenthe correlation coefficient is smaller than the threshold value TC.

[0110] Next, a detailed description will be provided referring to FIG.16. S400 and S416 are I/O to this process. S401, S402, S407 and S408 areloops for repeating calculations of correlated values for all imagedata. S403 is a denominator calculation in the formula (3-1). Thisdenominator corresponds to standard deviations.

[0111] S404 checks whether this standard deviation value is higher thana specified threshold value. When the value is small, the contrast willcorrespond to low contrast areas thereby becoming a target. In (S406)S405, the numerator of the formula (3-1) is also solved as a next stepand then this result divided using the already solved standarddeviation. S409, S410, S414 and S415 form a loop to repeat S411, S412and S413 for all pixels. These three steps determine whether colordistortion exists by comparing the correlated value solved above to aspecified threshold value.

[0112] Now, detection results of each method for original images areshown in FIG. 10. In the figure, the original image A1 has a colorregion on the right side and is provided with a monochrome line pair onthe left side. In this figure, color bleeding (color distortion) existsin the monochrome line pair although this cannot be seen. A2, A3 and A4are results detected by the correlation method, color saturation methodand the CIE L*a*b* method, respectively, and although a few efficiencydifferences appear, basically the detection can be performed.

[0113]FIG. 10 shows results of color distortion detection. The targetimages are Sample A (300×100 pixels), Sample B (200×200 pixels) andSample C (600×100 pixels). The white regions in A2, A3 and A4 of FIG. 10are areas detected as color distortion. The detection threshold valueswere empirically set as follows.

[0114] Width W that has the correlation in the correlation method usedin the third and seventh embodiments was three pixels. In addition, thethreshold value Td of the denominator was 1000 and the threshold valueTd of the correlation was 0.97.

[0115] The number of sequential pixels N determined to be colordistortion in the color saturation method used in the first and fifthembodiments was four pixels. The threshold value of the color saturationwas T_(S)=T_(Sr)=0.173.

[0116] In the color and monochrome separation using the CIE L*a*b*method used in the second and sixth embodiments, the size m of theseparation region was 50 pixels, and the threshold value of the edgedetection was 400. The accumulation value in the vertical direction, theaccumulation value in the horizontal direction and range of the a* valueand b* value of the color distortion determination are as shown in FIG.9.

[0117] In the general composition comprising the color scanner 1,computer 2, display 3 and printer 4 shown in FIG. 1, the previouslydescribed detection method can be utilized as a program by loading itinto internal memory, such as a hard disk, of the computer 2. As amatter of course, even if the detection method program is not loadedinitially, it can be copied to CD or FDD and then written to thecomputer 2. In addition, the program can also be transferred from anelectronic path such as the Internet or LAN. If necessary, it can betransferred or held resident in memory. For this case, the computer 2that has the program installed is an image determination apparatus thatfunctions to detect color distortion. This is the first, second andthird embodiments comprised as an image determination apparatus.Furthermore, the algorithms of the program used here implement themethods of the fifth, sixth and seventh embodiments.

[0118] Although it is not shown in the detailed figure, theabove-mentioned color distortion detection can be performed in the samecomposition as FIG. 1 using firmware of this color scanner or theprogram implemented in the hardware. For the composition of FIG. 1fourth embodiment is an image reading apparatus (color scanner) equippedwith a function to detect color distortion as an image determinationapparatus.

[0119] As shown in FIG. 2, this color distortion detection function canbe made independent and interfaced as a unit. This can be an imagedetermination apparatus that detects color distortion.

[0120] A description of designing a unit as described above issimplified referring to FIG. 20. As an easy way to design a unit, asingle chip microcomputer is used with the methods of the alreadydescribed fifth, sixth and seventh embodiments loaded into a program andthen written to a program ROM. As previously described, a page memoryfor at least three primary colors is necessary. Color saturation,histograms, and correlated values must be held in this memory. Becauseof this, installing a sufficient amount of work memory is alsonecessary.

[0121] Next, as an image reading apparatus of the fourth embodiment thecolor scanner 50 equipped with a color distortion detection means andthe ADF 90 that includes a reading function will be described referringto FIG. 17 and FIG. 18. For convenience sake, there are instances wherethe reading direction of the image sensor will be described as theprimary scanning direction. However, depending on a design of theoptical system as previously described, the photoelectric transducerscanning direction of the image sensor will be a direction differentfrom the direction of the lines which read the document. Consequently,the direction of the document reading lines is defined as the primaryscanning direction and the sub-scanning direction (direction of movementrelative to the document and reading lines) intersects the primaryscanning direction at a right angle.

[0122] This will be described referring to FIG. 17, FIG. 18 and FIG. 19.Documents, whose top surface will be read and loaded onto the paper feedtray 67, are grabbed by the pickup roller 52, and then the document feedroller 53 passes the document under the adhesion image sensor 60(hereinafter referred to as CIS 60) from the reading front roller 54.After passing under the CIS 60, the document passes under the backplaten 65. Then, the document is sent onto the discharge tray 66 by thedischarge roller 55. At this time, the optical carriage 57 that moves onthe rails of the color scanner 50 moves the reading position under theback platen 65 setting up a state that allows reads.

[0123] The optical carriage 56 is equipped with an image sensor 57 (thathas a three line CCD), a lens 58, a mirror 59, and a light source 69.Further, the CIS 60 is comprised of a light source (not shown in thefigure), a SELFOC lens group 62, three equal sensor lines equipped witha color sensor, and a substrate 61 equipped with three primary colorportions.

[0124] The three primary color image data produced from the CIS 60 issent to the image processing substrate for ADF 70. A color distortiondetection means (described later) is loaded in the image processingsubstrate for ADF 70. In the same manner, the three primary color imagedata is sent to the image processing substrate for color scanner 80. Acolor distortion detection means (described later) is also loaded in theimage processing substrate for color scanner 80. In the figure, 82 is aSCSI connector used to interface to a computer.

[0125] Next, the circuit composition will be described referring to FIG.18. To start, image data for each color is input to the image processingsubstrate for ADF 70 from the CIS 60. The data digitized for each colorby the A/D converter 110 is input to the shading correction means 111wherein shading correction is performed for each color. The data fromthe shading correction means 111 is input to the line intervalcorrection means 112 wherein each eight line segment is superimposedwhile using a work memory (not shown in the figure). Then, the data,whose reading position is corrected, is stored in the page memory 113.

[0126] Hereupon, an outline based on the block diagram of FIG. 18 willbe described. The three primary color image data stored in the pagememory 113 is managed by the CPU 119, and the address control unit 115examines the values of every pixel starting from the first line of theread until the last line of the read, and the color distortion isdetected by the color distortion unit 118. This color distortiondetection unit 118 is implemented in hardware, thereby making itpossible to execute the color distortion detection method of the alreadydescribed fifth, sixth and seventh embodiments.

[0127] Basically, implementing the calculation functions of each processin hardware simplifies the work of the operators. In addition,implementing the functions in hardware can be done by installing aspecial one-chip microcomputer and loading a program as an eighthembodiment in memory, such as program ROM, even without actuallyimplementing it in hardware. FIG. 20 is a block diagram that shows anoutline of this.

[0128] The CPU 119 determines whether each pixel is examined and whetherthere is a location where color distortion occurred using the result inthe color distortion detection unit 118. A flag is then entered in thework memory located inside the memory shift control unit 117.

[0129] It displays 1 when color distortion is present at the location,and displays 0 if it does not exist. This operation is a repetitiveprocess for all lines of three colors, or in other words, three colorsfor each page. Consequently, the flags corresponding to all pixels aredetermined as 1 or 0. Naturally, the result of the contrast calculationunit 114 which is located in the work memory of the color distortiondetection unit 118 can be written to the memory in the memory shiftcontrol unit 117, or it is also perfectly acceptable to install aspecial memory for the same result. Using either of these memories isthe same and is only a simple design change.

[0130] The memory shift control unit 117 calculates the amount ofdistortion referring to the flags. The density value (pixel value) ineach pixel utilized in this calculation is executed while reading andwriting from the (previously described) work memory or page memory wherethe data is temporarily stored. This control, however, is jointlymanaged by the CPU 119 and the address control unit 115.

[0131] Data which has undergone color distortion correction using thistype of algorithm subsequently passes through the I/F control unit 120of the image processing substrate for ADF 70 and is transferred to theimage processing substrate for color scanner 80. The data is sent to acomputer connected as a host device from the connector 82 passingthrough a bus buffer skipped on the image processing substrate for colorscanner 80 on the receiving side and also passing through the SPC 139(SCSI interface controller). The sequence, such as process or color,sent to the computer at this time is dependent on the software operatingon the computer. Therefore, there is a buffer memory 141 thattemporarily buffers the data in order to allow it to be replaced oranother similar action taken. This secession of processes is correctlyheld in synchronous order by the clock control unit 116.

[0132] The processes in the image processing substrate for color scanner80 are basically the same as those in the image processing substrate forADF 70, so details of them will be omitted. However, image signals fromthe image sensor 57 (three line color CCD) are digitized by the A/Dconverter 130 and undergone shading correction by the shading correctionunit 131. The outputs of this are made consistent by the line intervalcorrection means 132 and then temporarily stored in the page memory 133.

[0133] Because the data are utilized by the color distortion detectionunit 138 and the contrast calculation unit 134, color distortion isdetermined jointly by the CPU 139 and the address controller 135, andthe color is matched by memory shift control means 137. This secessionof processes is made synchronous and the processing advanced by theclock control unit 136.

[0134] Image data that underwent final color matching is stored togetherwith image data received from the image processing substrate for ADF 70in the buffer memory 142. While the transfer is being controlled by theCPU 139, the data are controlled by the SPC 140 and sent from theconnector 82 in synchronization with the application on the computerside in the same manner as the image data received from the imageprocessing substrate for ADF 70.

[0135] The following description is made referring to FIG. 17, FIG. 18and FIG. 19. A document stack is loaded onto the paper feed tray 14.When the conveyance starts, initially the document on the uppermostsurface is picked up by the pickup roller 16. The picked up document issent to the front read roller 18 by the supply roller 17.

[0136] The document that is further sent by the front read roller 18between the back platen 25 and the read window 13 is then sent to thedischarge roller. The document is then discharged to the discharge tray15 by the discharge roller 19. The surface of the back platen 25 iswhite. Consequently, the image data read by the sensor of the carriage12 appears white when there is no document or there are excessively readregions such as the periphery of a document.

[0137] In contrast, the optical carriage 12 that can move on a rail islocated inside the flatbed color scanner 10. A three line color CCD 22(Hereinafter referred to as image sensor 22. This image sensor need notbe a CCD. It can be a MOS or other type as well.), a lens 21, a mirror20, and a light source 24 are installed in the optical carriage 12.

[0138] When the document surface is lowered onto the glass copy stand 26and the loaded fixed document is read, the document surface is readwhile the carriage 12 moves in the SW direction. When the entiredocument is read, the optical carriage 12 returns in the BK directionand is then positioned, and stops based on a home position sensor (notshown in the figure).

[0139] In contrast, when reading a document sent to the above-mentionedADF 11, the optical carriage 12 moves to the lower surface of the readwindow 13 and then waits at the read position lined up with the readwindow 13. Thereafter, when a signal is received by the position sensorlocated close to the front read roller (not shown in the figure), theimage processing as well as the read processing circuit starts thereading. In the example shown in FIG. 2, a drive means, such as a pulsemotor, is located in the ADF 11 and controls the conveyance synchronouswith the operation of the flatbed color scanner 10.

[0140] The detailed operation was already described, and an outline ofthe operation flow is shown in FIG. 19. S119 is a start block, forexample, a read command from a personal computer. The ADF uses thelocation sensor of S200 to repeat document location detection untilreaching the read start position of S201.

[0141] Upon reaching the read start position, the CCD scanner of S202operates and reads the document. The data is then stored in page memory.After completing S203˜S205, the position of the three lines of the CCDwill be structurally different. Then, positioning using prime numbersonly is performed in S205, and the process completes in S207 moving tothe color distortion detection already described.

[0142]FIG. 1 shows an example of this correction process internallyprovided in a color scanner. As shown in, FIG. 2, however, the apparatus5 that has this image determination process that functions as colordistortion detection can be formed and installed in the latter part of acolor scanner. Of course, this color distortion detection function doesnot need to be located in any certain location. For example, it can belocated between the printer 4 and the computer 2 or within the printer4.

[0143] Storing this correction process as a program on a recordingmedium such as a CD-ROM and then distributing it, supplying it as asingle program via a network such as the Internet and downloading asnecessary, or providing a program upgrade service is all common sense inthis day and age.

What is claimed is:
 1. An image determination apparatus, comprising:image data receiving means for receiving image data comprising pixelsformed of RGB which are three primary colors of light; first comparingmeans for comparing a first color saturation value determined from colorcomponents of each pixel of the received image data to a first thresholdvalue, reversed image calculation means for calculating color componentsof reversed images acquired by subtracting said color components from apossible maximum value of said color components of each of said pixels,second comparing means for comparing a second color saturation valuedetermined from said color components of said reversed image to a secondthreshold value, and color region determination means for determiningwhether there is color or monochrome based on results of said firstcomparing means and said second comparing means.
 2. An imagedetermination apparatus according to claim 1, wherein said colorsaturation values are determined based on maximum values minimum valuesin the color components of said pixels.
 3. An image determinationapparatus according to claim 1, further comprising brightness changedetection means for examining changes in brightness from values of thecolor components of the pixels for regions said color regiondetermination means has determined to be monochrome, edge detectionmeans for detecting highly varying points in brightness obtained bycomparing the detected value from said brightness change detection meansand a specified value, and color saturation recalculation means forrecalculating color saturation values at said highly varying points inbrightness detected by said edge detection means.
 4. An imagedetermination apparatus according to claim 3, further comprising errordetection means for comparing a result of said color saturationrecalculation means to a specified value to determine errors in imagedata by counting a number of consecutive pixels which exceed a specifiedvalue.
 5. An image determination apparatus according to claim 1, furthercomprising error determination means for determining whether there iscolor or monochrome in said small regions determined to be monochrome bysaid color region determination means based on values of three types ofstandard deviations which use two reciprocal primary colors of the threecolor components using the pixels of the small regions, and also fordetermining if errors exist in the image data based on three types ofcorrelated values which use two reciprocal primary colors of the threecolor components of the pixels to be determined.
 6. An imagedetermination apparatus according to claim 5, wherein said errordetermination means calculates the correlated values as candidates forerror determination when the values of said standard deviations exceed aspecified value.
 7. An image determination apparatus, comprising: imagedata receiving means for receiving image data comprising pixels formedfrom RGB which are three primary colors of light, color conversion meansfor converting RGB color space of the received image to a uniformperceived color space that comprises brightness values in proportion toluminous intensity and standardized perceived color values in proportionto human coloring, accumulation means for accumulating the perceivedcolor values of the image data converted by said color conversion meansin each small-region comprised by specified pixels, and color regiondetermination means for determining whether said small regions are coloror monochrome based on the accumulated value.
 8. An image determinationapparatus according to claim 7, wherein said color conversion meansperforms conversions to CIE 1976 standard L*a*b* uniform perceived colorspace, and said color determining means determines whether said smallregions are color or monochrome based on the accumulated value of a*value and/or b* value of the image data converted to said uniformperceived color space within said small regions.
 9. An imagedetermination apparatus according to claim 7, further comprising errordetermination means for determining errors in the image data based onaccumulation of perceived color values in regions even smaller than saidregions including target pixels within small monochrome regions.
 10. Animage determination apparatus according to claim 7, wherein said errordetermination means determines that an error exists when the accumulatedvalue of a* value or the accumulated value of b* value of the targetpixels are outside a range of a specified value.
 11. An imagedetermination apparatus according to claim 7, further comprising errordetermination means for determining whether there is color or monochromein said small regions determined to be monochrome by said color regiondetermination means based on values of three types of standarddeviations which use two reciprocal primary colors of the three colorcomponents using the pixels of the small regions, and also fordetermining if errors exist in the image data based on three types ofcorrelated values which use two reciprocal primary colors of the threecolor components of the pixels to be determined.
 12. An imagedetermination apparatus according to claim 11, wherein said errordetermination means calculates the correlated values as candidates forerror determination when the values of said standard deviations exceed aspecified value.
 13. An image reading apparatus equipped with the imagedetermination apparatus according to claim
 1. 14. An image readingapparatus equipped with the image determination apparatus according toclaim
 7. 15. An image determination method, comprising the steps of: areceiving process for receiving image data accepting image data thatreceives image data comprising picture pixels formed of RGB which arethree primary colors of light, a first comparison process for comparinga first color saturation value determined from color components of eachpixel of said received image data to a first threshold value, reversedimage calculation process for calculating the color components ofreversed images acquired by subtracting said color components from apossible maximum value of the color components of each of said pixels,second comparing means for comparing a second color saturation valuedetermined from color components of the reversed image to a secondthreshold value, and color region determination means for determiningwhether there is color or monochrome based on a result of comparisonbetween said first comparing means and said second comparing means. 16.An image determination method according to claim 15, further comprisinga determination process for determining whether there is color ormonochrome in the regions determined to be monochrome by said colorregion determination means based on values of three types of standarddeviations which use two reciprocal primary colors of the three colorcomponents using the pixels of the small regions of a specified numberof pixels, and an error determination process for determining if errorsexist in the image data based on three types of correlated values whichuse two reciprocal primary colors of the three color components of thepixels to be determined.
 17. An image determination method, comprisingthe steps of: an image data receiving process for receiving image datacomprising pixels formed of RGB which are three primary colors of light;a color conversion process for converting RGB color space of thereceived image data to a uniform perceived color space that comprisesbrightness values in proportion to luminous intensity and standardizedperceived color values in proportion to human coloring, an accumulationprocess for accumulating perceived color values of image data convertedby said color conversion process in each small region comprised ofspecified pixels, and a color region determination process fordetermining whether the small regions are color or monochrome based onaccumulated result of said accumulation process.
 18. An imagedetermination method according to claim 17, further comprising adetermination process for determining whether there is color ormonochrome in the regions determined to be monochrome by said colorregion determination means based on values of three types of standarddeviations which use two reciprocal primary colors of the three colorcomponents using the pixels of the small regions of a specified numberof pixels, and an error determination process for determining if errorsexist in the image data based on three types of correlated values whichuse two reciprocal primary colors of the three color components of thepixels to be determined.